Overview

Dataset statistics

Number of variables14
Number of observations551
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.4 KiB
Average record size in memory112.2 B

Variable types

Numeric8
Categorical6

Alerts

name has a high cardinality: 549 distinct valuesHigh cardinality
host_name has a high cardinality: 395 distinct valuesHigh cardinality
neighbourhood has a high cardinality: 71 distinct valuesHigh cardinality
last_review has a high cardinality: 229 distinct valuesHigh cardinality
id is highly overall correlated with host_idHigh correlation
host_id is highly overall correlated with idHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
calculated_host_listings_count is highly overall correlated with neighbourhoodHigh correlation
neighbourhood_group is highly overall correlated with neighbourhoodHigh correlation
neighbourhood is highly overall correlated with calculated_host_listings_count and 1 other fieldsHigh correlation
name is uniformly distributedUniform
host_name is uniformly distributedUniform
id has unique valuesUnique
number_of_reviews has 17 (3.1%) zerosZeros

Reproduction

Analysis started2023-01-22 11:34:05.135103
Analysis finished2023-01-22 11:34:26.898856
Duration21.76 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct551
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83358.931
Minimum2539
Maximum204065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-01-22T16:34:27.139704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile9437.5
Q132008.5
median65268
Q3138920
95-th percentile190027
Maximum204065
Range201526
Interquartile range (IQR)106911.5

Descriptive statistics

Standard deviation59539.3
Coefficient of variation (CV)0.7142522
Kurtosis-1.049293
Mean83358.931
Median Absolute Deviation (MAD)43624
Skewness0.51628699
Sum45930771
Variance3.5449283 × 109
MonotonicityStrictly increasing
2023-01-22T16:34:27.461169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2539 1
 
0.2%
106647 1
 
0.2%
103161 1
 
0.2%
103311 1
 
0.2%
103806 1
 
0.2%
105469 1
 
0.2%
105510 1
 
0.2%
106363 1
 
0.2%
107630 1
 
0.2%
89427 1
 
0.2%
Other values (541) 541
98.2%
ValueCountFrequency (%)
2539 1
0.2%
2595 1
0.2%
3647 1
0.2%
3831 1
0.2%
5022 1
0.2%
5099 1
0.2%
5121 1
0.2%
5178 1
0.2%
5203 1
0.2%
5238 1
0.2%
ValueCountFrequency (%)
204065 1
0.2%
203901 1
0.2%
202273 1
0.2%
201992 1
0.2%
200955 1
0.2%
200645 1
0.2%
199312 1
0.2%
199195 1
0.2%
197942 1
0.2%
197753 1
0.2%

name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct549
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Loft w/ Terrace @ Box House Hotel
 
2
Superior @ Box House
 
2
Tree lined block modern apartment
 
1
2 BR w/ Terrace @ Box House Hotel
 
1
BOHEMIAN EAST VILLAGE 2 BED HAVEN
 
1
Other values (544)
544 

Length

Max length50
Median length45
Mean length33.818512
Min length3

Characters and Unicode

Total characters18634
Distinct characters90
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique547 ?
Unique (%)99.3%

Sample

1st rowClean & quiet apt home by the park
2nd rowSkylit Midtown Castle
3rd rowTHE VILLAGE OF HARLEM....NEW YORK !
4th rowCozy Entire Floor of Brownstone
5th rowEntire Apt: Spacious Studio/Loft by central park

Common Values

ValueCountFrequency (%)
Loft w/ Terrace @ Box House Hotel 2
 
0.4%
Superior @ Box House 2
 
0.4%
Tree lined block modern apartment 1
 
0.2%
2 BR w/ Terrace @ Box House Hotel 1
 
0.2%
BOHEMIAN EAST VILLAGE 2 BED HAVEN 1
 
0.2%
Oceanfront Apartment in Rockaway 1
 
0.2%
Private 1-Bedroom Apt in Townhouse 1
 
0.2%
Bright Room With A Great River View 1
 
0.2%
Clean & quiet apt home by the park 1
 
0.2%
Artsy TopFloor Apt in PRIME BEDFORD Williamsburg 1
 
0.2%
Other values (539) 539
97.8%

Length

2023-01-22T16:34:27.825469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in 134
 
4.4%
95
 
3.1%
apt 73
 
2.4%
room 70
 
2.3%
apartment 61
 
2.0%
private 57
 
1.9%
bedroom 54
 
1.8%
village 51
 
1.7%
cozy 49
 
1.6%
east 49
 
1.6%
Other values (695) 2364
77.3%

Most occurring characters

ValueCountFrequency (%)
2549
 
13.7%
e 1293
 
6.9%
o 1119
 
6.0%
t 1033
 
5.5%
a 1006
 
5.4%
r 956
 
5.1%
n 909
 
4.9%
i 872
 
4.7%
l 639
 
3.4%
s 482
 
2.6%
Other values (80) 7776
41.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11744
63.0%
Uppercase Letter 3526
 
18.9%
Space Separator 2549
 
13.7%
Other Punctuation 434
 
2.3%
Decimal Number 238
 
1.3%
Dash Punctuation 88
 
0.5%
Math Symbol 18
 
0.1%
Open Punctuation 9
 
< 0.1%
Close Punctuation 9
 
< 0.1%
Other Symbol 9
 
< 0.1%
Other values (3) 10
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1293
11.0%
o 1119
 
9.5%
t 1033
 
8.8%
a 1006
 
8.6%
r 956
 
8.1%
n 909
 
7.7%
i 872
 
7.4%
l 639
 
5.4%
s 482
 
4.1%
u 455
 
3.9%
Other values (16) 2980
25.4%
Uppercase Letter
ValueCountFrequency (%)
S 308
 
8.7%
B 307
 
8.7%
A 275
 
7.8%
C 250
 
7.1%
R 226
 
6.4%
L 216
 
6.1%
E 207
 
5.9%
T 163
 
4.6%
O 158
 
4.5%
N 158
 
4.5%
Other values (16) 1258
35.7%
Other Punctuation
ValueCountFrequency (%)
, 108
24.9%
! 80
18.4%
/ 68
15.7%
. 66
15.2%
& 42
 
9.7%
* 21
 
4.8%
' 18
 
4.1%
# 9
 
2.1%
: 8
 
1.8%
@ 7
 
1.6%
Other values (3) 7
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 81
34.0%
2 72
30.3%
3 27
 
11.3%
5 21
 
8.8%
0 17
 
7.1%
4 10
 
4.2%
8 3
 
1.3%
7 3
 
1.3%
9 2
 
0.8%
6 2
 
0.8%
Math Symbol
ValueCountFrequency (%)
+ 14
77.8%
> 2
 
11.1%
= 1
 
5.6%
~ 1
 
5.6%
Other Symbol
ValueCountFrequency (%)
6
66.7%
2
 
22.2%
1
 
11.1%
Dash Punctuation
ValueCountFrequency (%)
- 85
96.6%
3
 
3.4%
Space Separator
ValueCountFrequency (%)
2549
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 7
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%
Nonspacing Mark
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15270
81.9%
Common 3363
 
18.0%
Inherited 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1293
 
8.5%
o 1119
 
7.3%
t 1033
 
6.8%
a 1006
 
6.6%
r 956
 
6.3%
n 909
 
6.0%
i 872
 
5.7%
l 639
 
4.2%
s 482
 
3.2%
u 455
 
3.0%
Other values (42) 6506
42.6%
Common
ValueCountFrequency (%)
2549
75.8%
, 108
 
3.2%
- 85
 
2.5%
1 81
 
2.4%
! 80
 
2.4%
2 72
 
2.1%
/ 68
 
2.0%
. 66
 
2.0%
& 42
 
1.2%
3 27
 
0.8%
Other values (27) 185
 
5.5%
Inherited
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18618
99.9%
Misc Symbols 8
 
< 0.1%
Punctuation 6
 
< 0.1%
None 1
 
< 0.1%
VS 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2549
 
13.7%
e 1293
 
6.9%
o 1119
 
6.0%
t 1033
 
5.5%
a 1006
 
5.4%
r 956
 
5.1%
n 909
 
4.9%
i 872
 
4.7%
l 639
 
3.4%
s 482
 
2.6%
Other values (73) 7760
41.7%
Misc Symbols
ValueCountFrequency (%)
6
75.0%
2
 
25.0%
Punctuation
ValueCountFrequency (%)
3
50.0%
2
33.3%
1
 
16.7%
None
ValueCountFrequency (%)
1
100.0%
VS
ValueCountFrequency (%)
1
100.0%

host_id
Real number (ℝ)

Distinct472
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean362242.5
Minimum2787
Maximum6197784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-01-22T16:34:28.161091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2787
5-th percentile17778
Q187991
median276291
Q3572239.5
95-th percentile909190
Maximum6197784
Range6194997
Interquartile range (IQR)484248.5

Descriptive statistics

Standard deviation409585.08
Coefficient of variation (CV)1.1306931
Kurtosis77.979214
Mean362242.5
Median Absolute Deviation (MAD)204277
Skewness6.2506866
Sum1.9959562 × 108
Variance1.6775993 × 1011
MonotonicityNot monotonic
2023-01-22T16:34:28.479622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
417504 6
 
1.1%
204539 5
 
0.9%
256161 5
 
0.9%
72062 4
 
0.7%
303939 4
 
0.7%
22486 4
 
0.7%
209460 3
 
0.5%
322716 3
 
0.5%
126607 3
 
0.5%
54275 3
 
0.5%
Other values (462) 511
92.7%
ValueCountFrequency (%)
2787 1
0.2%
2845 1
0.2%
4396 1
0.2%
4632 1
0.2%
4869 1
0.2%
7192 1
0.2%
7310 1
0.2%
7322 1
0.2%
7355 1
0.2%
7356 1
0.2%
ValueCountFrequency (%)
6197784 1
0.2%
3088389 1
0.2%
2248897 1
0.2%
1856604 1
0.2%
1000477 1
0.2%
988350 1
0.2%
973438 1
0.2%
971075 1
0.2%
964482 1
0.2%
961342 1
0.2%

host_name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct395
Distinct (%)71.8%
Missing1
Missing (%)0.2%
Memory size4.4 KiB
Mark
 
7
Jason
 
6
Daniel
 
6
The Box House Hotel
 
6
Jessica
 
5
Other values (390)
520 

Length

Max length23
Median length19
Mean length6.1527273
Min length1

Characters and Unicode

Total characters3384
Distinct characters59
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique305 ?
Unique (%)55.5%

Sample

1st rowJohn
2nd rowJennifer
3rd rowElisabeth
4th rowLisaRoxanne
5th rowLaura

Common Values

ValueCountFrequency (%)
Mark 7
 
1.3%
Jason 6
 
1.1%
Daniel 6
 
1.1%
The Box House Hotel 6
 
1.1%
Jessica 5
 
0.9%
Jennifer 5
 
0.9%
Wayne 5
 
0.9%
Alex 5
 
0.9%
Sarah 4
 
0.7%
Lisel 4
 
0.7%
Other values (385) 497
90.2%

Length

2023-01-22T16:34:28.796334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14
 
2.2%
and 10
 
1.6%
mark 7
 
1.1%
daniel 6
 
0.9%
the 6
 
0.9%
box 6
 
0.9%
house 6
 
0.9%
hotel 6
 
0.9%
jason 6
 
0.9%
alex 5
 
0.8%
Other values (405) 560
88.6%

Most occurring characters

ValueCountFrequency (%)
a 392
 
11.6%
e 347
 
10.3%
n 273
 
8.1%
i 263
 
7.8%
r 204
 
6.0%
l 187
 
5.5%
o 139
 
4.1%
s 130
 
3.8%
t 121
 
3.6%
h 95
 
2.8%
Other values (49) 1233
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2638
78.0%
Uppercase Letter 638
 
18.9%
Space Separator 82
 
2.4%
Other Punctuation 25
 
0.7%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 392
14.9%
e 347
13.2%
n 273
10.3%
i 263
10.0%
r 204
7.7%
l 187
 
7.1%
o 139
 
5.3%
s 130
 
4.9%
t 121
 
4.6%
h 95
 
3.6%
Other values (18) 487
18.5%
Uppercase Letter
ValueCountFrequency (%)
A 64
 
10.0%
S 59
 
9.2%
J 55
 
8.6%
M 54
 
8.5%
L 52
 
8.2%
T 39
 
6.1%
D 37
 
5.8%
C 36
 
5.6%
E 32
 
5.0%
B 29
 
4.5%
Other values (13) 181
28.4%
Other Punctuation
ValueCountFrequency (%)
& 15
60.0%
. 5
 
20.0%
" 2
 
8.0%
' 1
 
4.0%
, 1
 
4.0%
/ 1
 
4.0%
Space Separator
ValueCountFrequency (%)
82
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3276
96.8%
Common 108
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 392
 
12.0%
e 347
 
10.6%
n 273
 
8.3%
i 263
 
8.0%
r 204
 
6.2%
l 187
 
5.7%
o 139
 
4.2%
s 130
 
4.0%
t 121
 
3.7%
h 95
 
2.9%
Other values (41) 1125
34.3%
Common
ValueCountFrequency (%)
82
75.9%
& 15
 
13.9%
. 5
 
4.6%
" 2
 
1.9%
' 1
 
0.9%
, 1
 
0.9%
+ 1
 
0.9%
/ 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3377
99.8%
None 7
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 392
 
11.6%
e 347
 
10.3%
n 273
 
8.1%
i 263
 
7.8%
r 204
 
6.0%
l 187
 
5.5%
o 139
 
4.1%
s 130
 
3.8%
t 121
 
3.6%
h 95
 
2.8%
Other values (46) 1226
36.3%
None
ValueCountFrequency (%)
ú 3
42.9%
é 2
28.6%
í 2
28.6%
Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Brooklyn
260 
Manhattan
253 
Queens
 
25
Bronx
 
8
Staten Island
 
5

Length

Max length13
Median length9
Mean length8.3702359
Min length5

Characters and Unicode

Total characters4612
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrooklyn
2nd rowManhattan
3rd rowManhattan
4th rowBrooklyn
5th rowManhattan

Common Values

ValueCountFrequency (%)
Brooklyn 260
47.2%
Manhattan 253
45.9%
Queens 25
 
4.5%
Bronx 8
 
1.5%
Staten Island 5
 
0.9%

Length

2023-01-22T16:34:29.064428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-22T16:34:29.360003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 260
46.8%
manhattan 253
45.5%
queens 25
 
4.5%
bronx 8
 
1.4%
staten 5
 
0.9%
island 5
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n 809
17.5%
a 769
16.7%
o 528
11.4%
t 516
11.2%
r 268
 
5.8%
B 268
 
5.8%
l 265
 
5.7%
y 260
 
5.6%
k 260
 
5.6%
M 253
 
5.5%
Other values (10) 416
9.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4051
87.8%
Uppercase Letter 556
 
12.1%
Space Separator 5
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 809
20.0%
a 769
19.0%
o 528
13.0%
t 516
12.7%
r 268
 
6.6%
l 265
 
6.5%
y 260
 
6.4%
k 260
 
6.4%
h 253
 
6.2%
e 55
 
1.4%
Other values (4) 68
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
B 268
48.2%
M 253
45.5%
Q 25
 
4.5%
S 5
 
0.9%
I 5
 
0.9%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4607
99.9%
Common 5
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 809
17.6%
a 769
16.7%
o 528
11.5%
t 516
11.2%
r 268
 
5.8%
B 268
 
5.8%
l 265
 
5.8%
y 260
 
5.6%
k 260
 
5.6%
M 253
 
5.5%
Other values (9) 411
8.9%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4612
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 809
17.5%
a 769
16.7%
o 528
11.4%
t 516
11.2%
r 268
 
5.8%
B 268
 
5.8%
l 265
 
5.7%
y 260
 
5.6%
k 260
 
5.6%
M 253
 
5.5%
Other values (10) 416
9.0%

neighbourhood
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct71
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Williamsburg
63 
East Village
48 
Harlem
46 
Bedford-Stuyvesant
40 
Greenpoint
 
26
Other values (66)
328 

Length

Max length25
Median length17
Mean length11.800363
Min length4

Characters and Unicode

Total characters6502
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)4.5%

Sample

1st rowKensington
2nd rowMidtown
3rd rowHarlem
4th rowClinton Hill
5th rowEast Harlem

Common Values

ValueCountFrequency (%)
Williamsburg 63
 
11.4%
East Village 48
 
8.7%
Harlem 46
 
8.3%
Bedford-Stuyvesant 40
 
7.3%
Greenpoint 26
 
4.7%
Upper West Side 25
 
4.5%
Park Slope 20
 
3.6%
Crown Heights 18
 
3.3%
Chelsea 18
 
3.3%
Hell's Kitchen 17
 
3.1%
Other values (61) 230
41.7%

Length

2023-01-22T16:34:29.630561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east 92
 
10.3%
village 65
 
7.3%
williamsburg 63
 
7.1%
harlem 59
 
6.6%
side 53
 
5.9%
heights 42
 
4.7%
bedford-stuyvesant 40
 
4.5%
upper 38
 
4.3%
west 37
 
4.1%
slope 30
 
3.4%
Other values (79) 374
41.9%

Most occurring characters

ValueCountFrequency (%)
e 671
 
10.3%
l 508
 
7.8%
i 462
 
7.1%
a 435
 
6.7%
s 409
 
6.3%
t 407
 
6.3%
r 393
 
6.0%
342
 
5.3%
o 280
 
4.3%
n 269
 
4.1%
Other values (40) 2326
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5153
79.3%
Uppercase Letter 945
 
14.5%
Space Separator 342
 
5.3%
Dash Punctuation 44
 
0.7%
Other Punctuation 18
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 146
15.4%
S 143
15.1%
W 112
11.9%
E 93
9.8%
V 65
6.9%
C 64
6.8%
B 63
6.7%
G 59
6.2%
U 40
 
4.2%
P 34
 
3.6%
Other values (14) 126
13.3%
Lowercase Letter
ValueCountFrequency (%)
e 671
13.0%
l 508
9.9%
i 462
9.0%
a 435
 
8.4%
s 409
 
7.9%
t 407
 
7.9%
r 393
 
7.6%
o 280
 
5.4%
n 269
 
5.2%
g 196
 
3.8%
Other values (12) 1123
21.8%
Other Punctuation
ValueCountFrequency (%)
' 17
94.4%
. 1
 
5.6%
Space Separator
ValueCountFrequency (%)
342
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6098
93.8%
Common 404
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 671
 
11.0%
l 508
 
8.3%
i 462
 
7.6%
a 435
 
7.1%
s 409
 
6.7%
t 407
 
6.7%
r 393
 
6.4%
o 280
 
4.6%
n 269
 
4.4%
g 196
 
3.2%
Other values (36) 2068
33.9%
Common
ValueCountFrequency (%)
342
84.7%
- 44
 
10.9%
' 17
 
4.2%
. 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6502
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 671
 
10.3%
l 508
 
7.8%
i 462
 
7.1%
a 435
 
6.7%
s 409
 
6.3%
t 407
 
6.3%
r 393
 
6.0%
342
 
5.3%
o 280
 
4.3%
n 269
 
4.1%
Other values (40) 2326
35.8%

room_type
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Entire home/apt
321 
Private room
225 
Shared room
 
5

Length

Max length15
Median length15
Mean length13.738657
Min length11

Characters and Unicode

Total characters7570
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowPrivate room
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 321
58.3%
Private room 225
40.8%
Shared room 5
 
0.9%

Length

2023-01-22T16:34:29.902428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-22T16:34:30.173081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
entire 321
29.1%
home/apt 321
29.1%
room 230
20.9%
private 225
20.4%
shared 5
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 872
11.5%
t 867
11.5%
o 781
10.3%
r 781
10.3%
a 551
 
7.3%
551
 
7.3%
m 551
 
7.3%
i 546
 
7.2%
h 326
 
4.3%
p 321
 
4.2%
Other values (7) 1423
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6147
81.2%
Space Separator 551
 
7.3%
Uppercase Letter 551
 
7.3%
Other Punctuation 321
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 872
14.2%
t 867
14.1%
o 781
12.7%
r 781
12.7%
a 551
9.0%
m 551
9.0%
i 546
8.9%
h 326
 
5.3%
p 321
 
5.2%
n 321
 
5.2%
Other values (2) 230
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
E 321
58.3%
P 225
40.8%
S 5
 
0.9%
Space Separator
ValueCountFrequency (%)
551
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6698
88.5%
Common 872
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 872
13.0%
t 867
12.9%
o 781
11.7%
r 781
11.7%
a 551
8.2%
m 551
8.2%
i 546
8.2%
h 326
 
4.9%
p 321
 
4.8%
E 321
 
4.8%
Other values (5) 781
11.7%
Common
ValueCountFrequency (%)
551
63.2%
/ 321
36.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 872
11.5%
t 867
11.5%
o 781
10.3%
r 781
10.3%
a 551
 
7.3%
551
 
7.3%
m 551
 
7.3%
i 546
 
7.2%
h 326
 
4.3%
p 321
 
4.2%
Other values (7) 1423
18.8%

price
Real number (ℝ)

Distinct103
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.79211
Minimum33
Maximum239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-01-22T16:34:30.427143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile50.5
Q190
median120.79
Q3140.5
95-th percentile200
Maximum239
Range206
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation43.817112
Coefficient of variation (CV)0.36274814
Kurtosis-0.13600929
Mean120.79211
Median Absolute Deviation (MAD)28.21
Skewness0.38874247
Sum66556.451
Variance1919.9393
MonotonicityNot monotonic
2023-01-22T16:34:30.730104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121.8139535 71
 
12.9%
120.79 54
 
9.8%
150 24
 
4.4%
200 15
 
2.7%
99 14
 
2.5%
120 14
 
2.5%
75 13
 
2.4%
125 13
 
2.4%
130 13
 
2.4%
80 12
 
2.2%
Other values (93) 308
55.9%
ValueCountFrequency (%)
33 1
 
0.2%
35 1
 
0.2%
36 2
0.4%
37 3
0.5%
39 2
0.4%
40 2
0.4%
42 3
0.5%
43 1
 
0.2%
44 2
0.4%
45 1
 
0.2%
ValueCountFrequency (%)
239 1
 
0.2%
235 1
 
0.2%
230 1
 
0.2%
229 1
 
0.2%
228 1
 
0.2%
225 8
1.5%
220 1
 
0.2%
219 2
 
0.4%
215 2
 
0.4%
211 1
 
0.2%

minimum_nights
Real number (ℝ)

Distinct35
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9285517
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-01-22T16:34:31.029556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q37
95-th percentile30
Maximum200
Range199
Interquartile range (IQR)5

Descriptive statistics

Standard deviation15.755246
Coefficient of variation (CV)1.9871531
Kurtosis69.016251
Mean7.9285517
Median Absolute Deviation (MAD)1
Skewness6.9581576
Sum4368.632
Variance248.22779
MonotonicityNot monotonic
2023-01-22T16:34:31.314579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 136
24.7%
3 115
20.9%
1 63
11.4%
4 50
 
9.1%
30 33
 
6.0%
5 33
 
6.0%
7 27
 
4.9%
7.928 19
 
3.4%
6 13
 
2.4%
14 11
 
2.0%
Other values (25) 51
 
9.3%
ValueCountFrequency (%)
1 63
11.4%
2 136
24.7%
3 115
20.9%
4 50
 
9.1%
5 33
 
6.0%
6 13
 
2.4%
7 27
 
4.9%
7.928 19
 
3.4%
8 3
 
0.5%
9 3
 
0.5%
ValueCountFrequency (%)
200 1
 
0.2%
180 1
 
0.2%
90 3
0.5%
65 1
 
0.2%
60 1
 
0.2%
50 1
 
0.2%
45 3
0.5%
44 1
 
0.2%
40 1
 
0.2%
35 1
 
0.2%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct171
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.034246
Minimum0
Maximum227
Zeros17
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-01-22T16:34:31.632217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q122
median66
Q394
95-th percentile197.5
Maximum227
Range227
Interquartile range (IQR)72

Descriptive statistics

Standard deviation59.359421
Coefficient of variation (CV)0.84757706
Kurtosis0.13570718
Mean70.034246
Median Absolute Deviation (MAD)40
Skewness0.98209929
Sum38588.87
Variance3523.5408
MonotonicityNot monotonic
2023-01-22T16:34:31.979758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.03426124 52
 
9.4%
70.034 32
 
5.8%
0 17
 
3.1%
1 10
 
1.8%
11 9
 
1.6%
9 9
 
1.6%
4 8
 
1.5%
10 8
 
1.5%
18 7
 
1.3%
2 7
 
1.3%
Other values (161) 392
71.1%
ValueCountFrequency (%)
0 17
3.1%
1 10
1.8%
2 7
1.3%
3 6
 
1.1%
4 8
1.5%
5 5
 
0.9%
6 4
 
0.7%
7 4
 
0.7%
8 6
 
1.1%
9 9
1.6%
ValueCountFrequency (%)
227 4
0.7%
226 1
 
0.2%
225 1
 
0.2%
223 1
 
0.2%
222 2
0.4%
220 1
 
0.2%
219 1
 
0.2%
218 1
 
0.2%
214 2
0.4%
213 1
 
0.2%

last_review
Categorical

Distinct229
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
7/1/2019
 
38
6/23/2019
 
26
6/24/2019
 
19
6/16/2019
 
15
6/22/2019
 
14
Other values (224)
439 

Length

Max length10
Median length9
Mean length8.7749546
Min length8

Characters and Unicode

Total characters4835
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique153 ?
Unique (%)27.8%

Sample

1st row10/19/2018
2nd row5/21/2019
3rd row7/1/2019
4th row7/5/2019
5th row11/19/2018

Common Values

ValueCountFrequency (%)
7/1/2019 38
 
6.9%
6/23/2019 26
 
4.7%
6/24/2019 19
 
3.4%
6/16/2019 15
 
2.7%
6/22/2019 14
 
2.5%
6/30/2019 11
 
2.0%
6/20/2019 11
 
2.0%
6/29/2019 10
 
1.8%
6/26/2019 9
 
1.6%
6/28/2019 8
 
1.5%
Other values (219) 390
70.8%

Length

2023-01-22T16:34:32.289112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/1/2019 38
 
6.9%
6/23/2019 26
 
4.7%
6/24/2019 19
 
3.4%
6/16/2019 15
 
2.7%
6/22/2019 14
 
2.5%
6/30/2019 11
 
2.0%
6/20/2019 11
 
2.0%
6/29/2019 10
 
1.8%
6/26/2019 9
 
1.6%
6/28/2019 8
 
1.5%
Other values (219) 390
70.8%

Most occurring characters

ValueCountFrequency (%)
/ 1102
22.8%
1 896
18.5%
2 834
17.2%
0 629
13.0%
9 449
9.3%
6 305
 
6.3%
7 170
 
3.5%
5 135
 
2.8%
8 125
 
2.6%
3 115
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3733
77.2%
Other Punctuation 1102
 
22.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 896
24.0%
2 834
22.3%
0 629
16.8%
9 449
12.0%
6 305
 
8.2%
7 170
 
4.6%
5 135
 
3.6%
8 125
 
3.3%
3 115
 
3.1%
4 75
 
2.0%
Other Punctuation
ValueCountFrequency (%)
/ 1102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 1102
22.8%
1 896
18.5%
2 834
17.2%
0 629
13.0%
9 449
9.3%
6 305
 
6.3%
7 170
 
3.5%
5 135
 
2.8%
8 125
 
2.6%
3 115
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 1102
22.8%
1 896
18.5%
2 834
17.2%
0 629
13.0%
9 449
9.3%
6 305
 
6.3%
7 170
 
3.5%
5 135
 
2.8%
8 125
 
2.6%
3 115
 
2.4%

reviews_per_month
Real number (ℝ)

Distinct227
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0487151
Minimum0.01
Maximum6.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-01-22T16:34:32.639575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.06
Q10.3
median0.8
Q31.435
95-th percentile3.015
Maximum6.7
Range6.69
Interquartile range (IQR)1.135

Descriptive statistics

Standard deviation0.99990361
Coefficient of variation (CV)0.95345595
Kurtosis4.6275907
Mean1.0487151
Median Absolute Deviation (MAD)0.53
Skewness1.8022455
Sum577.842
Variance0.99980723
MonotonicityNot monotonic
2023-01-22T16:34:33.008562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.054 43
 
7.8%
0.11 9
 
1.6%
0.1 7
 
1.3%
0.03 7
 
1.3%
0.21 6
 
1.1%
0.22 6
 
1.1%
0.16 6
 
1.1%
0.08 6
 
1.1%
0.09 6
 
1.1%
0.38 6
 
1.1%
Other values (217) 449
81.5%
ValueCountFrequency (%)
0.01 4
0.7%
0.02 4
0.7%
0.03 7
1.3%
0.04 5
0.9%
0.05 5
0.9%
0.06 4
0.7%
0.07 5
0.9%
0.08 6
1.1%
0.09 6
1.1%
0.1 7
1.3%
ValueCountFrequency (%)
6.7 1
0.2%
6.62 1
0.2%
4.72 1
0.2%
4.64 1
0.2%
4.58 1
0.2%
4.5 1
0.2%
4.44 1
0.2%
4.34 1
0.2%
4.22 1
0.2%
4.1 1
0.2%
Distinct12
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0509074
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-01-22T16:34:33.296585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum28
Range27
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.0583937
Coefficient of variation (CV)1.4912393
Kurtosis54.742189
Mean2.0509074
Median Absolute Deviation (MAD)0
Skewness6.9107659
Sum1130.05
Variance9.3537719
MonotonicityNot monotonic
2023-01-22T16:34:33.516546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 341
61.9%
2 106
 
19.2%
3 48
 
8.7%
4 17
 
3.1%
5 14
 
2.5%
6 13
 
2.4%
28 6
 
1.1%
8 2
 
0.4%
2.05 1
 
0.2%
7 1
 
0.2%
Other values (2) 2
 
0.4%
ValueCountFrequency (%)
1 341
61.9%
2 106
 
19.2%
2.05 1
 
0.2%
3 48
 
8.7%
4 17
 
3.1%
5 14
 
2.5%
6 13
 
2.4%
7 1
 
0.2%
8 2
 
0.4%
11 1
 
0.2%
ValueCountFrequency (%)
28 6
 
1.1%
13 1
 
0.2%
11 1
 
0.2%
8 2
 
0.4%
7 1
 
0.2%
6 13
 
2.4%
5 14
 
2.5%
4 17
 
3.1%
3 48
8.7%
2.05 1
 
0.2%

availability_365
Real number (ℝ)

Distinct205
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.51361
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-01-22T16:34:33.789922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.5
Q166
median246
Q3346
95-th percentile365
Maximum365
Range364
Interquartile range (IQR)280

Descriptive statistics

Standard deviation132.98335
Coefficient of variation (CV)0.6257639
Kurtosis-1.4146678
Mean212.51361
Median Absolute Deviation (MAD)119
Skewness-0.34015513
Sum117095
Variance17684.57
MonotonicityNot monotonic
2023-01-22T16:34:34.106092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 109
 
19.8%
189 8
 
1.5%
188 7
 
1.3%
6 7
 
1.3%
34 7
 
1.3%
12 6
 
1.1%
2 6
 
1.1%
364 6
 
1.1%
3 5
 
0.9%
31 5
 
0.9%
Other values (195) 385
69.9%
ValueCountFrequency (%)
1 4
0.7%
2 6
1.1%
3 5
0.9%
5 1
 
0.2%
6 7
1.3%
7 3
0.5%
8 2
 
0.4%
9 4
0.7%
11 1
 
0.2%
12 6
1.1%
ValueCountFrequency (%)
365 109
19.8%
364 6
 
1.1%
363 2
 
0.4%
362 2
 
0.4%
361 1
 
0.2%
359 4
 
0.7%
358 1
 
0.2%
355 4
 
0.7%
353 3
 
0.5%
351 1
 
0.2%

Interactions

2023-01-22T16:34:23.287211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:08.166990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:10.576888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:12.714056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:14.710063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:16.933664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:19.000416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:21.167284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:23.572862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:08.645467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:10.866448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:12.984213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:15.010470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:17.208509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:19.283067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:21.452258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:23.846656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:08.926668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:11.130186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:13.232293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:15.284940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:17.461164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:19.558894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:21.718292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:24.097218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:09.178836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:11.366507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:13.455018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:15.535355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:17.694457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:19.802541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:21.952467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:24.954387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:09.479741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:11.651707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:13.719837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:15.831660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:17.963367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:20.092108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:22.233247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:25.212641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:09.731388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:11.899956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:13.960063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:16.090861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:18.210511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:20.345314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:22.491297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:25.482848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:10.025827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:12.177668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:14.217493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:16.376298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:18.476088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:20.628893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:22.756123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:25.749890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:10.297776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:12.431932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:14.463551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:16.645149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:18.728426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:20.892106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-22T16:34:23.016804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-22T16:34:34.394447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
idhost_idpriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365neighbourhood_groupneighbourhoodroom_type
id1.0000.8260.0240.043-0.062-0.038-0.0320.0520.0900.0920.071
host_id0.8261.0000.0440.070-0.098-0.050-0.1390.0480.0000.0000.000
price0.0240.0441.0000.126-0.108-0.150-0.227-0.0500.1790.1690.430
minimum_nights0.0430.0700.1261.000-0.273-0.300-0.149-0.0030.0000.0000.000
number_of_reviews-0.062-0.098-0.108-0.2731.0000.7360.156-0.1940.1210.0050.025
reviews_per_month-0.038-0.050-0.150-0.3000.7361.0000.115-0.1150.1140.0000.064
calculated_host_listings_count-0.032-0.139-0.227-0.1490.1560.1151.0000.0190.2640.5330.107
availability_3650.0520.048-0.050-0.003-0.194-0.1150.0191.0000.0390.1200.042
neighbourhood_group0.0900.0000.1790.0000.1210.1140.2640.0391.0000.9380.081
neighbourhood0.0920.0000.1690.0000.0050.0000.5330.1200.9381.0000.127
room_type0.0710.0000.4300.0000.0250.0640.1070.0420.0810.1271.000

Missing values

2023-01-22T16:34:26.158527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-22T16:34:26.643666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodroom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
02539Clean & quiet apt home by the park2787JohnBrooklynKensingtonPrivate room149.001.09.00000010/19/20180.2106.00365
12595Skylit Midtown Castle2845JenniferManhattanMidtownEntire home/apt225.001.045.0000005/21/20190.3802.00355
23647THE VILLAGE OF HARLEM....NEW YORK !4632ElisabethManhattanHarlemPrivate room150.003.00.0000007/1/20191.0541.00365
33831Cozy Entire Floor of Brownstone4869LisaRoxanneBrooklynClinton HillEntire home/apt89.001.070.0342617/5/20194.6401.00194
45022Entire Apt: Spacious Studio/Loft by central park7192LauraManhattanEast HarlemEntire home/apt80.0010.09.00000011/19/20180.1002.05365
55099Large Cozy 1 BR Apartment In Midtown East7322ChrisManhattanMurray HillEntire home/apt200.003.074.0000006/22/20190.5901.00129
65121BlissArtsSpace!7356GaronBrooklynBedford-StuyvesantPrivate room60.0045.049.00000010/5/20170.4001.00365
75178Large Furnished Room Near B'way8967ShunichiManhattanHell's KitchenPrivate room120.792.070.03426110/6/20173.4701.0022
85203Cozy Clean Guest Room - Family Apt7490MaryEllenManhattanUpper West SidePrivate room120.792.0118.00000010/7/20170.9901.00365
95238Cute & Cozy Lower East Side 1 bdrm7549BenManhattanChinatownEntire home/apt150.001.0160.00000010/8/20171.3304.00188
idnamehost_idhost_nameneighbourhood_groupneighbourhoodroom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
541197753Large room in elevator drman bldg964482ColinManhattanHarlemPrivate room68.0000002.070.0346/12/20193.9002.0188
542197942Comfy, Cozy, Brooklyn close to Manhattan289135ToniBrooklynBedford-StuyvesantEntire home/apt99.0000003.070.0347/7/20192.1801.034
543199195Modern Bedroom in Hamilton Heights971075JabariManhattanHarlemPrivate room75.0000003.050.0003/30/20190.6502.038
544199312Sunny Space in Williamsburg973438SusanneBrooklynWilliamsburgPrivate room100.0000002.070.0346/24/20192.8501.0254
545200645Best Manhattan Studio Deal!933378EdoManhattanUpper East SideShared room90.0000001.070.0347/1/20191.0541.0365
546200955STYLISH EAST VILLAGE FLAT568325SimoneManhattanEast VillageEntire home/apt160.00000030.025.0004/30/20180.2601.046
547201992Serene Park Slope Garden Apartment988350AndreaBrooklynPark SlopeEntire home/apt190.0000004.0105.0005/11/20191.6201.0328
548202273Cozy and spacious - rare for NYC!918087KestrelBrooklynBedford-StuyvesantPrivate room67.0000004.072.00012/26/20160.7603.0365
549203901Beautiful UES apartment1000477ElizabethManhattanUpper East SideEntire home/apt190.0000001.08.0009/18/20160.0801.0365
550204065St. James Pl Garden Studio 1block to PRATT &Gtrain51038EricaBrooklynClinton HillEntire home/apt121.8139532.058.0006/4/20190.6106.0199